NaileR (version 1.2.0)

nail_sort: Sort textual data

Description

Group textual data according to their similarity, in a context in which the assessors have commented on a set of stimuli.

Usage

nail_sort(
  dataset,
  name_size = 3,
  stimulus_id = "stimulus",
  introduction = "",
  measure = "",
  nb_max = 6,
  generate = FALSE
)

Value

A list consisting of:

• a list of prompts (one per assessor);

• a data frame with the group names.

Arguments

dataset

a data frame where each row is a stimulus and each column is an assessor.

name_size

the maximum number of words in a group name created by the LLM.

stimulus_id

the nature of the stimulus. Customizing it is highly recommended.

introduction

the introduction to the LLM prompt.

measure

the type of measure used in the experiment.

nb_max

the maximum number of clusters the LLM can form per assessor.

generate

a boolean that indicates whether to generate the LLM response. If FALSE, the function only returns the prompt.

Details

This function uses a while loop to ensure that the LLM gives the right number of groups. Therefore, customizing the stimulus ID, prompt introduction and measure is highly recommended; a clear prompt can help the LLM finish its task faster.

Examples

if (FALSE) {
# Processing time is often longer than ten seconds
# because the function uses a large language model.

library(NaileR)
data(beard_wide)

intro_beard <- "As a barber, you make
recommendations based on consumers comments.
Examples of consumers descriptions of beards
are as follows."
intro_beard <- gsub('\n', ' ', intro_beard) |>
stringr::str_squish()

res <- nail_sort(beard_wide[,1:5], name_size = 3,
stimulus_id = "beard", introduction = intro_beard,
measure = 'the description was', generate = TRUE)

res$dta_sort
cat(res$prompt_llm[[1]])
}